Characterization and Prediction of Welding Droplet Release Using Time Series Data Mining

This paper presents the results from characterizing and predicting the release of droplets of metal from a welder. The welding process joins two pieces of metal into one by making a joint between them. An arcing current melts the tip of a wire, forming a metal droplet that elongates until it releases. The goal is to predict the moment when a droplet will release, which can improve the quality of the joint by allowing the droplet releases to be monitored and controlled. Because of the irregular, chaotic, and event nature of the droplet release, prediction is impossible using traditional time series methods. Using Time Series Data Mining techniques allows the droplet releases to be predicted with a high degree of accuracy. The Time Series Data Mining (TSDM) framework (Povinelli 1999; Povinelli and Feng 1998; Povinelli and Feng 1999) is applied to the prediction of welding droplet releases. Methods based on the TSDM framework are able to successfully characterize and predict complex, nonperiodic, irregular, and chaotic time series such as the release of metal droplets from a welder. This paper, which is divided into three sections, presents the results of applying the TSDM framework to this problem. The first section discusses the welding problem. The second section reviews the key TSDM concepts and an extension of the TSDM framework to multiple temporal patterns. The third section presents the prediction results. PROBLEM STATEMENT Welding joins two pieces of metal by forming a joint between them. As illustrated in Figure 1, a current arc is created between the welder and the metal to be joined. Wire is pushed out of the welder. The tip of the wire melts, forming a metal droplet that elongates (sticks out) until it releases. Predicting when a droplet of metal will release from a welder allows the quality of the metal joint to be monitored and controlled. The problem is to predict the releases { } , 1, t Y y t N = = , where t is a time index, and N is the number of observations, using the stickout { } , 1, t X x t N = = time series. This time series is a 1 Drs. C. Tolle, E. Larsen, D. Pace, and D. Iosty of INEEL gathered the data used in this paper. Their work was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Materials and Materials Engineering, under DOE Idaho Operations Office Contract DE-AC07-94ID13223. measure of the droplet elongation. Because of the irregular, chaotic, and noisy nature of the droplet release, prediction is impossible using traditional time series methods. Figure 1 – Welding Process A sample of the stickout time series is illustrated in Figure 2. An electronic camera on the welding station measures the droplet stickout in pixels. It is sampled at 1kHz and comprised of approximately 5,000 observations. The release time series, also illustrated in Figure 2, indicates the release of a droplet (event) with a one and a non-release (nonevent) with a zero. It is synchronized with the stickout time series. Figure 2 – Welding Stickout and Release Time Series TIME SERIES DATA MINING METHOD Previous work (Povinelli 1999; Povinelli and Feng 1998; Povinelli and Feng 1999) presented the TSDM framework. Here the TSDM method for identifying multiple temporal pattern clusters is discussed. The TSDM method discussed here discovers hidden temporal patterns (vectors of length Q) characteristic of events (important occurrences) by time-delay embedding (Abarbanel 1996; Iwanski and Bradley 1998; Tolle 1997; Tolle and Gundersen 1998) an observed time series X into a reconstructed phase space, here simply called phase space. An event characterization function g is used to represent the eventness of a temporal pattern. An augmented phase space is formed by extending the phase space with g. The 150 160 170 180 190 200 40 0 42 0 44 0 46 0 48 0 50 0 52 0 54 0 56 0 58 0 60 0 t x t (p ix el s) stickout